#!/usr/bin/python3
# coding=utf-8
#
# Copyright (C) 2023-2025. Huawei Technologies Co., Ltd. All rights reserved.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# ===============================================================================

import numpy as np
import torch
from torch.nn import functional as F

dtype_emu = {torch.bfloat16: 0, torch.float16: 1, torch.float32: 2, torch.int8: 3, torch.int16: 4, torch.int32: 5}

def gen_golden_data_simple():
    dtype = torch.float32
    # dtype = bfloat16

    ## 核间均分，单核计算量对齐:
    # input_shape = [32, 1024]

    ## 核间均分，单核计算量非对齐:
    # input_shape = [8, 1023]

    ## 核间不均分，单核计算量对齐:
    # input_shape = [32, 1023]

    ## 核间不均分，单核计算量非对齐:
    input_shape = [17, 1023]
    input_src = (torch.rand(17, 1023) * (10 - (-10)) + (-10)).to(dtype=dtype)
    golden = np.full(input_src.shape, 4.8, dtype=np.float32)

    tiling = np.array([input_shape[0] * input_shape[1], dtype_emu[dtype]], dtype=np.uint32)

    tiling.tofile("./input/input_tiling.bin")
    input_src.numpy().tofile("./input/input_x.bin")
    golden.tofile("./output/golden.bin")

if __name__ == "__main__":
    gen_golden_data_simple()
